mlsanityData to 7 Oct 2026

Position: AI Scaling: From Up to Down and Out

arXiv 2502.01677 ↗First posted 2 Feb 2025cs.LG cs.AIcited by 5

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Abstract (arXiv)

AI Scaling has traditionally been synonymous with Scaling Up, which builds larger and more powerful models. However, the growing demand for efficiency, adaptability, and collaboration across diverse applications necessitates a broader perspective. This position paper presents a holistic framework for AI scaling, encompassing Scaling Up, Scaling Down, and Scaling Out. It argues that while Scaling Up of models faces inherent bottlenecks, the future trajectory of AI scaling lies in Scaling Down and Scaling Out. These paradigms address critical technical and societal challenges, such as reducing carbon footprint, ensuring equitable access, and enhancing cross-domain collaboration. We explore transformative applications in healthcare, smart manufacturing, and content creation, demonstrating how AI Scaling can enable breakthroughs in efficiency, personalization, and global connectivity. Additionally, we highlight key challenges, including balancing model complexity with interpretability, managing resource constraints, and fostering ethical development. By synthesizing these approaches, we propose a unified roadmap that redefines the future of AI research and application, paving the way for advancements toward Artificial General Intelligence (AGI).

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 61 of its 83 references that are in the map. First posted on arXiv 2 Feb 2025.

Builds on

61 of 83 references.

References · 61

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38 more
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50 most cited of 61 All 61 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2026HiCI: Hierarchical Construction-Integration for Long-Context Attentioncited by 2
  2. 2025Evaluating the Application of SOLID Principles in Modern AI Framework Architecturescited by 0
  3. 2026Selecting Language Models for Social Science: Start Small, Start Open, and Validatecited by 0
  4. 2026Position: Stop Preaching and Start Practising Data Frugality for Responsible Development of AIcited by 0
  5. 2026An Investigation of the NeurIPS and ICML 2025 Position Trackscited by 0